Quantifying Uncertainty in Predictions of Hepatic Clearance

Abstract

Preclinical predictions of human pharmacokinetic parameters are routinely used in pharmaceutical research and development. In particular, pharmacokinetic predictions are critical in the decision to advance a potential drug to the clinic, to determine appropriate dosing regimens for first-in-human studies, and as a component of translational pharmacology models. Although the associated biological and mathematical models have been extensively discussed in the pharmacokinetic literature, relatively little work has been done to explicitly relate the estimation error of these methods to the underlying experimental variability. This article proposes and evaluates Bayesian models for this purpose.

We apply our methodology to a dataset describing both preclinical and clinical pharmacokinetic experimentation for 12 different anonymized drugs. For each drug and for each preclinical mode of prediction, a credible interval is computed and compared against estimates obtained by direct experimentation with human subjects in the clinic. We conclude that many apparent translational differences may be readily explained as a function of experimental error.

We view this problem as representative of a larger class of statistical problems in translational medicine, where the mathematics of translation from one species to another requires multiple experimentally estimated scaling factors.

Keywords: :

• Bayesian
• Pharmacokinetics
• Preclinical
• Translational